Dense vs MoE Models: How Nemotron 3.5 Lightning Activates Only 3B of 30B Parameters

A Dense model uses all of its parameters to generate each token. An MoE model splits the network into experts and routes each token to only a few of them, cutting compute while retaining the capacity of a much larger model.

Active parameters are the parameters actually used to compute a single token. In Nemotron 3.5 Lightning, about 3 billion of 30 billion parameters are active per token, and that figure drives compute cost and latency.

Yes, especially for on-premises or regional cloud deployments, because fewer active parameters means fewer GPUs and lower running costs while preserving high output quality.

No. Dense models are simpler to deploy and fine-tune and can win on peak-quality tasks, while MoE wins on high-throughput, cost-per-token efficiency.